The University of Southampton
University of Southampton Institutional Repository

Stem cell differentiation as a non-Markov stochastic process

Stem cell differentiation as a non-Markov stochastic process
Stem cell differentiation as a non-Markov stochastic process
Pluripotent stem cells can self-renew in culture and differentiate along all somatic lineages in vivo. While much is known about the molecular basis of pluripotency, the mechanisms of differentiation remain unclear. Here, we profile individual mouse embryonic stem cells as they progress along the neuronal lineage. We observe that cells pass from the pluripotent state to the neuronal state via an intermediate epiblast-like state. However, analysis of the rate at which cells enter and exit these observed cell states using a hidden Markov model indicates the presence of a chain of unobserved molecular states that each cell transits through stochastically in sequence. This chain of hidden states allows individual cells to record their position on the differentiation trajectory, thereby encoding a simple form of cellular memory. We suggest a statistical mechanics interpretation of these results that distinguishes between functionally distinct cellular ‘macrostates’ and functionally similar molecular ‘microstates’, and propose a model of stem cell differentiation as a non-Markov stochastic process.
2405-4712
268-282
Stumpf, Patrick
dfdb286c-b321-46d3-8ba2-85b3b4a7f092
Smith, Rosanna C.G.
1fe5586f-92e9-4658-bd55-cd3eaa176b66
Lenz, Michael
9d9cde0c-f2e9-433a-8d2c-5fe21b6dad0f
Schuppert, Andreas, A.
62f9eb46-e8ed-424b-a4ae-9dadd63f8a31
Muller, Franz-Josef
14203be1-092e-47ae-a023-d4c798adc449
Babtie, Ann
633b7500-61dc-4f8e-977a-2831951c5775
Chan, Thalia E.
705579a4-008e-4b9e-b7e4-506d839a0072
Stumpf, Michael P.H.
8320fd41-8650-4438-92aa-0b70ebf7cc00
Please, Colin P.
118dffe7-4b38-4787-a972-9feec535839e
Howison, Sam D.
5cce2c2e-abd3-4878-8a42-e471bfc966de
Arai, Fumio
55b7859b-98f0-450a-b355-74e1f1e1945d
Macarthur, Benjamin D.
2c0476e7-5d3e-4064-81bb-104e8e88bb6b
Stumpf, Patrick
dfdb286c-b321-46d3-8ba2-85b3b4a7f092
Smith, Rosanna C.G.
1fe5586f-92e9-4658-bd55-cd3eaa176b66
Lenz, Michael
9d9cde0c-f2e9-433a-8d2c-5fe21b6dad0f
Schuppert, Andreas, A.
62f9eb46-e8ed-424b-a4ae-9dadd63f8a31
Muller, Franz-Josef
14203be1-092e-47ae-a023-d4c798adc449
Babtie, Ann
633b7500-61dc-4f8e-977a-2831951c5775
Chan, Thalia E.
705579a4-008e-4b9e-b7e4-506d839a0072
Stumpf, Michael P.H.
8320fd41-8650-4438-92aa-0b70ebf7cc00
Please, Colin P.
118dffe7-4b38-4787-a972-9feec535839e
Howison, Sam D.
5cce2c2e-abd3-4878-8a42-e471bfc966de
Arai, Fumio
55b7859b-98f0-450a-b355-74e1f1e1945d
Macarthur, Benjamin D.
2c0476e7-5d3e-4064-81bb-104e8e88bb6b

Stumpf, Patrick, Smith, Rosanna C.G., Lenz, Michael, Schuppert, Andreas, A., Muller, Franz-Josef, Babtie, Ann, Chan, Thalia E., Stumpf, Michael P.H., Please, Colin P., Howison, Sam D., Arai, Fumio and Macarthur, Benjamin D. (2017) Stem cell differentiation as a non-Markov stochastic process. Cell Systems, 5 (3), 268-282. (doi:10.1016/j.cels.2017.08.009).

Record type: Article

Abstract

Pluripotent stem cells can self-renew in culture and differentiate along all somatic lineages in vivo. While much is known about the molecular basis of pluripotency, the mechanisms of differentiation remain unclear. Here, we profile individual mouse embryonic stem cells as they progress along the neuronal lineage. We observe that cells pass from the pluripotent state to the neuronal state via an intermediate epiblast-like state. However, analysis of the rate at which cells enter and exit these observed cell states using a hidden Markov model indicates the presence of a chain of unobserved molecular states that each cell transits through stochastically in sequence. This chain of hidden states allows individual cells to record their position on the differentiation trajectory, thereby encoding a simple form of cellular memory. We suggest a statistical mechanics interpretation of these results that distinguishes between functionally distinct cellular ‘macrostates’ and functionally similar molecular ‘microstates’, and propose a model of stem cell differentiation as a non-Markov stochastic process.

Text
memories_paper_short - Accepted Manuscript
Download (7MB)
Text
1-s2.0-S2405471217303423-main - Version of Record
Available under License Creative Commons Attribution.
Download (2MB)

More information

Accepted/In Press date: 7 August 2017
e-pub ahead of print date: 27 September 2017
Published date: 27 September 2017

Identifiers

Local EPrints ID: 416970
URI: http://eprints.soton.ac.uk/id/eprint/416970
ISSN: 2405-4712
PURE UUID: 380a9766-46e9-45e8-b2ec-db42b3f43613
ORCID for Patrick Stumpf: ORCID iD orcid.org/0000-0003-0862-0290

Catalogue record

Date deposited: 15 Jan 2018 17:31
Last modified: 17 Dec 2019 05:55

Export record

Altmetrics

Contributors

Author: Patrick Stumpf ORCID iD
Author: Rosanna C.G. Smith
Author: Michael Lenz
Author: Andreas, A. Schuppert
Author: Franz-Josef Muller
Author: Ann Babtie
Author: Thalia E. Chan
Author: Michael P.H. Stumpf
Author: Colin P. Please
Author: Sam D. Howison
Author: Fumio Arai

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×